AI RESEARCH
RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models
arXiv CS.LG
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ArXi:2603.20711v1 Announce Type: cross Vision-Language-Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) deployment offers an effective fix by easing edge-device computing pressure to meet real-time needs. However, existing ECC frameworks are suboptimal for VLA models due to two challenges: (1) Diverse model structures hinder optimal ECC segmentation point identification; (2) Even if the optimal split point is determined, changes in network bandwidth can cause performance drift.